(Georgia Institute of Technology, 1999-09)
Mavris, Dimitri N.; Roth, Bryce Alexander; Macsotai, Noel I.
The objective of this paper is to illustrate how probabilistic methods can be utilized to rationally and analytically make design decisions in the presence of uncertainty, with emphasis on the use of probabilistic sensitivities in the aircraft gas turbine engine preliminary design process. A brief review of risk and uncertainty in the engine design process is given, and the role of probabilistic methods is discussed. Probabilistic sensitivity analysis, used in conjunction with response surface methods, is proposed as a computationally-efficient method to address defined sources of uncertainty and risk in engine design from a system level perspective. The method outlined is then applied to the analysis of engine component performance uncertainty impact on the performance of a notional four-engine wide-body commercial transport. More specifically, uncertainty in engine design parameters is shown to have a direct and quantifiable impact on aircraft system figures of merit such as design range and fuel burn. The methods developed are then used to create a set of contour plots showing the behavior of vehicle performance uncertainty over the design space of interest.
(Georgia Institute of Technology, 1998-09)
Mavris, Dimitri N.; Macsotai, Noel I.; Roth, Bryce Alexander
The objective of this paper is to examine ways in which to implement probabilistic design methods in the aircraft engine preliminary design process. Specifically, the focus is on analytically determining the impact of uncertainty in engine component performance on the overall performance of a notional large commercial transport, particularly the impact on design range, fuel burn, and engine weight. The emphasis is twofold: first is to find ways to reduce the impact of this uncertainty through appropriate engine cycle selections, and second is on finding ways to leverage existing design margin to squeeze more performance out of current technology. One of the fundamental results shown herein is that uncertainty in component performance has a significant impact on the overall aircraft performance (it is on t he same order of magnitude as the impact of the cycle itself). However, this paper shows that uncertainties in component efficiencies, pressure losses, and cooling flow losses do not have a significant influence on the variance of aircraft performance. This paper also shows that the probabilistic method is very useful for formulating direct trades of design margin against performance or other figures of merit such as engine weight, thus enabling the existing design margin to be capitalized upon in the interest of obtaining better system performance. In terms of a comparison between techniques, one can conclude that the probabilistic approach is inherently more computationally intensive that the deterministic approach. It therefore behooves the designer to choose wisely when setting up the problem in order to avoid unnecessary work. However, a properly formulated probabilistic method provides a much clearer picture of how the various system trades another and enables the ultimate cycle selection to be analytically determined based on the level of risk that is consistent with program objectives.